Learning based controller for autonomous driving

ABSTRACT

In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to a learning based controller for autonomous driving.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

A vehicle controller of an autonomous vehicle may generate controlcommands to move the vehicle according to a desired path or route. Thecontroller may utilize static control algorithms.

A model predictive controller can generate a sequence of commands to beapplied over future time frames that would cause a controlled object tomove along a predicted path. The sequence of commands can be optimizedwith respect to different terms, such as, for example, cross-trackerror, heading error, and sudden changes in velocity, acceleration,heading, etc. The first of the sequence of commands is applied to thecontrolled object. At a subsequent time (e.g., the next cycle), thisprocess is repeated and the first of the new sequence of commands isapplied to the controlled object at each cycle.

Such a controller can be used to control and autonomous driving vehicle(ADV), to track along a target path with target speeds. An MPC (modelpredictive control) may use static optimization algorithms and a staticvehicle model to generate the optimized sequence of commands. Thesecontrol algorithms, however, may not account for changes in thevehicle's environment or changes in the vehicle. No matter what thevehicle's environment is and how much the vehicle physical statuschanges, the controller will use the same algorithm to achieve a desiredspeed and steering behavior. This can reduce safety and cause anuncomfortable driving experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 shows a block diagram illustrating system architecture forautonomous driving according to one embodiment.

FIG. 5 shows a process for controlling an autonomous driving vehiclebased on scenario, according to one embodiment.

FIG. 6 shows a learning based model predictive controller according toone embodiment.

FIG. 7 shows a system for controlling an autonomous driving vehiclebased on a learning based model predictive controller according to oneembodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, an on-line learning system updatesparameters for a controller (e.g., a model predictive controller) basedon a current physical environment of the autonomous driving vehicle, anda current physical status of the ADV. An MPC can generate a series ofcontrol commands, optimized based on predicted movements of the ADV.These predicted movements will track a target path or route (includingposition and heading along way points on the route) while optimizingterms to reduce undesirable conditions (e.g., large changes in speed,acceleration, and heading). Thus, control commands are optimized by theMPC and, when applied to ADV control actuators, will cause the ADV totrack the target path.

These control commands are then applied to a vehicle dynamic model thatrepresents the ADV, to simulate how the ADV will respond. In thesimulation, real-time environmental conditions are accounted for, suchas vehicle traffic, road conditions (e.g., wetness, slipperiness, ice),sensed obstacles, etc. The ADV may also have passengers or other cargoitems that increase the mass of the ADV. Fuel, battery state of chargeor battery state of health may also change. These changes can beaccounted for in the simulation. The optimization parameters (e.g.,weights or coefficients) of the MPC controller are evaluated andadjusted based on the simulation.

The adjusted MPC controller is used to generate one or more controlcommands that account for current environmental conditions of the ADV.This adjustment can occur periodically, e.g., on a per-driving cyclebasis. In this manner, the MPC controller is adjusted ‘on-line’,periodically, while driving. Other aspects and details are discussed inthe present disclosure.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using Wi-Fi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, algorithms 124 may includealgorithms used by a model predictive controller of the presentdisclosure. Algorithms 124 can then be uploaded on ADVs to be utilizedduring autonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, and learning based MPC 308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 miles per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Learning based MPC 308 includes an MPC controller having an optimizerand a vehicle model. The optimizer can use a cost function and thevehicle model to generate a sequence of control commands (e.g.,throttle, steering, and/or brake commands) that track the vehicle's pathalong a target vehicle trajectory. These commands are generated whileoptimizing for different cost terms (e.g., cross-track error, headingerror, speed, steering, acceleration, rate of change in steering, and/orrate of change of acceleration). Each of the cost terms can berepresented in the cost function to penalize undesirable behavior.Weights can be associated and applied to each term (e.g.,multiplication) to modify the impact of each term over the overallcomputed cost.

Learning based MPC module can be an on-line learning module, meaningthat it continuously updates the MPC controller based on new data (e.g.,the vehicle's environment and physical parameters), e.g., while thevehicle is driving. Learning-based MPC 308 may be integrated withanother module, such as, for example, planning module 305 and/or controlmodule 306.

A generic example of a cost function of an MPC controller is shownbelow, where J is the total computed cost, wx is a weight correspondingto a term x (x=1, 2, . . . ), and N is a point along the targettrajectory of the ADV.

J=Σ _(t=1) ^(N) w ₁∥term1∥² +w ₂∥term2∥² +w ₃∥term3∥²++Σ_(t=1) ^(N−1) w₄∥term4∥² +w ₅∥term5∥²+Σ_(t=2) ^(N) w ₆∥term6∥² +w ₇∥term7∥²+ . . .

The terms can be optimized by minimizing a computed cost J. The termscan include at least one of cross-track error (penalizing how far theADV is from the target trajectory), heading error (penalizing an errorbetween the ADV heading and the target trajectory direction at a point),speed cost (penalizing changes to speed), steering cost (penalizingchanges in steering), acceleration cost (penalizing changes inacceleration), steering rate of change (penalizing how fast the steeringchanges), braking cost (penalizing braking), and acceleration rate ofchange (penalizing how fast acceleration changes). In some embodiments,the cost function includes at least two of the above terms. In otherembodiments, the cost function includes all of the above terms.Sequential control commands (throttle, steering, braking) can begenerated to optimally track the target trajectory, while accounting forthe above terms.

These terms are generated by the MPC optimizer by using a static modelof the ADV. The MPC predicts how the static model will move along thetrajectory, in response to different control commands, to determine anoptimized sequence of control commands based on the static model of theADV, and minimizing the cost function.

The MPC, by itself, does not account for environmental factors or acurrent physical status in the ADV, both of which can vary while thevehicle travels to an intended destination along the trajectory. Thevehicle model of the MPC controller can be a simplified vehicle modelthat includes the vehicle's size and mass. This vehicle model can bestatic, meaning that it is constant over time (e.g., from one frame toanother frame).

The learning based MPC controller 308 feeds these control commands thatare generated with the static MPC controller, into a vehicle dynamicmodel that includes the vehicle's mass and geometry (e.g., shape andsize), but also includes additional dynamic parameters that model howthe ADV will respond to different control commands and under differentenvironmental conditions and under different physical status of the ADV(e.g., full tank, empty tank, low battery charge, wet brakes, etc.). Thedynamic model also more accurately determines how the ADV will respondto particular commands (e.g., at higher speeds vs lower speeds) and atlarger steering angles. The dynamic vehicle model can be generated fromactual testing of a physical ADV, to more accurately represent how theADV will behave under different speeds, road conditions, steeringangles, with different passenger and cargo loads, etc.

The learning-based MPC 308 adjusts the model predictive controller basedon evaluation of how the vehicle dynamic model behaves in response tothe control commands. The weights of the MPC can be adjusted. Thisadjusted model predictive controller can then be used to generate acontrol command for the physical ADV. This can be repeated periodically,e.g., on a per-driving cycle basis. More aspects and details arediscussed in other sections.

FIG. 4 is a block diagram illustrating system architecture forautonomous driving according to one embodiment. System architecture 400may represent system architecture of an autonomous driving system asshown in FIGS. 3A and 3B. Referring to FIG. 4, system architecture 400includes, but it is not limited to, application layer 401, planning andcontrol (PNC) layer 402, perception layer 403, driver layer 404,firmware layer 405, and hardware layer 406. Application layer 401 mayinclude user interface or configuration application that interacts withusers or passengers of an autonomous driving vehicle, such as, forexample, functionalities associated with user interface system 113. PNClayer 402 may include functionalities of at least planning module 305and control module 306. Perception layer 403 may include functionalitiesof at least perception module 302. In one embodiment, there is anadditional layer including the functionalities of prediction module 303and/or decision module 304. Alternatively, such functionalities may beincluded in PNC layer 402 and/or perception layer 403. Systemarchitecture 400 further includes driver layer 404, firmware layer 405,and hardware layer 406. Firmware layer 405 may represent at least thefunctionality of sensor system 115, which may be implemented in a formof a field programmable gate array (FPGA). Hardware layer 406 mayrepresent the hardware of the autonomous driving vehicle such as controlsystem 111. Layers 401-403 can communicate with firmware layer 405 andhardware layer 406 via device driver layer 404.

FIG. 5 shows a process 500 for controlling an ADV with a learning-basedmodel predictive controller, according to some embodiments. Operation501 includes generating a control command with an MPC controller, theMPC controller including a cost function with one or more weightsassociated with cost terms of the cost function. This control commandmay not yet be adjusted according to the current environmentalconditions and physical attributes of the ADV.

Operation 502 includes applying the control command to a dynamic modelof the ADV to simulate behavior of the ADV. Although the MPC controllerused in operation 501 may include a simplified model of the ADV, thedynamic model can more accurately model behavior of the ADV byaccounting for more nuanced and non-linear behavior of the ADV, whichcan be determined through testing and experimentation.

Further, although not required, simulation of the dynamic model can beperformed in a virtual environment that can include objects andstructures that are currently sensed around the ADV, to account for theADV's current environment when generating control commands. The virtualenvironment can include a two-dimensional or three-dimensionalrepresentation of a current environment around the ADV. Althoughsimplified, this environment can include geometry that definesboundaries of objects (e.g., pedestrians, vehicles, structures), as wellas road boundaries. This virtual environment can be generated based onsensed data from sensor system 115, and/or information from map androute information 311, localization module 301, and other modules fromperception and planning system 110.

Operation 503 includes adjusting the one or more weights based on theevaluation of the dynamic model in response to the control command,resulting in an adjusted cost function of the MPC controller. Forexample, this operation can determine one or more scores associated withthe one or more weights based on evaluation of the dynamic model inresponse to the control command. The evaluation can also be solely basedon one or more environmental conditions around the ADV, such as wetness,vehicle traffic, etc. As discussed, the MPC controller used at block 501can include a cost function that has terms that are optimized whiletracking the ADV along a target trajectory. These terms can each haveassociated weights to adjust how much emphasis is placed on each term,e.g., relative to the other. Each term can be evaluated (e.g. scored)based on how the dynamic model behaves in the simulated environment.

For example, if environmental conditions are wet, causing the ADV toslip more than usual, then the steering term can be evaluated withdisfavor (low scores), to discourage steering changes. In return, theweights associated with these terms may then be adjusted with anincrease to further penalize steering under wet conditions. The adjustedMPC here will generate new control commands with less aggressivesteering.

In another example, analysis of the dynamic model in a simulatedenvironment may show that the dynamic model moves too close to sensedobjects like curbs, pedestrians, or other vehicles. Again, terms will beevaluated and weights for cross-tracking and heading errors may beincreased to further penalize these errors so that the ADV moreaggressively tracks along the target trajectory. When the objects are nolonger sensed, then these weights can be lowered (e.g., in subsequentcycles) to relax the ADV controls and improve ADV ride comfort.

In another example, the ADV may sense that traffic is light. Under theseconditions, the weighting associated with a speed term can be reduced sothat speed increases are penalized less. Thus, the learning based MPCcan increase speed more aggressively (e.g., towards a speed limitconstraint) when traffic is light. Conversely, when traffic is high, theweight associated with the speed cost term can be increased, to furtherpenalize faster speeds.

In another example, when traffic is high, the weights associated withbraking may be decreased, to reduce penalty for braking. It should beunderstood that although ‘high’ traffic and ‘low’ traffic are relativeterms; thresholds or other known mechanisms can be implemented to deducewhether traffic is low or high.

In another example, the ADV experiences physical changes while driving.For example, brakes can get hot or wet, thereby changing the vehicle'sresponse to brake commands. The ADV may take on passengers or cargo thatchange the mass of the ADV. An amount of gasoline in the ADV's tank, ora state of charge or state of health of an electric ADV may also changethe way the vehicle responds to throttle, steering and brake commands.The same result can result in different ADV behavior, when the ADVexperiences physical changes. Changes to physical attributes of the ADVcan be sensed by ADV's sensor system and used to update the dynamicmodel of the ADV and/or used to directly evaluate the weights of thecost terms. Thus, the dynamic model and/or the weights can berepresentative of the current state of the ADV.

Operation 504 includes generating, with the MPC controller having theadjusted cost function, a second control command used to effect movementof the ADV. In such a manner, the learning-based MPC controller uses acontrol algorithm that is optimized for current physical attributes ofthe ADV and the ADV's current environment. It should be understood thatthe ADV's current environment includes the ADV's effective environment,for example, within an effective perimeter around the ADV that isrelevant to the ADV's control command. This can be, for example, 10meters, 20 meters, 30 meters, 40 meters, etc. Such a perimeter can varybased on application and can be determined through routine test andexperimentation.

FIG. 6 shows a learning-based MPC module 308 according to someembodiments. A reference is provided to a model predictive controlmodule 602. The reference can include, for example, a target ADVtrajectory, current heading, and current speed of the ADV.

The MPC module 602 can include a vehicle model 670 and a cost function674. The cost function can include cost terms 678, and associatedweights 676. The MPC module can generate a sequence of future commands672 (e.g., throttle, brake, and steering) that will predictively effectmovement of the vehicle model such that the vehicle model tracks thereference, while minimizing the cost function.

A first of the sequence of commands is taken and applied to a vehicledynamic model 606 to simulate a more accurate representation of vehiclebehavior than that which is generated at MPC module 602. As discussed,this vehicle dynamic model 606 can include dynamic response of the ADVthat can be determined based on testing of actual ADVs. Such responsesmay be non-linear and more accurately reflect the ADV's dynamic behaviorthan the vehicle model 670 of the MPC module 602. As mentioned, thevehicle dynamic model may also include physical attributes of the ADVthat may vary during driving (e.g., brake wetness, fuel state, batterystate, passengers and cargo). The vehicle dynamic model can haveadjusted responses to control commands based on these physicalattributes.

An environment module 608 can gather information describingenvironmental conditions 609 currently around the ADV, such as apedestrian, another vehicle, a road boundary, or a structure, roadconditions (wetness, slipperiness, ice, snow), weather conditions, andvehicle traffic.

A parameter evaluation module 610 can evaluate the vehicle dynamicbehavior with respect to the reference, for example, to see how well thevehicle dynamic model tracked the trajectory. The vehicle dynamic modelcan generate a different trajectory from the predicted trajectory thatis generated by MPC module 602, because the vehicle models aredifferent. At parameter evaluation module 610, behavior of the dynamicmodel's response to the control command can be evaluated with respect tothe current environmental conditions.

In some embodiments, evaluation of the dynamic model in response to thecontrol command is based on at least one of: proximity of the dynamicmodel to a pedestrian, another vehicle, or a structure; a speed of thedynamic model with reference to a speed constraint; an acceleration ofthe dynamic model with respect to an acceleration constraint; a locationof the dynamic model with respect to a road boundary or path; changes inheading, or speed that can cause ride discomfort; and control effort.Scores 611 can be generated for the weights of the cost function, basedon how well each of the terms performed in the dynamic model. Forexample, if the predicted path of the dynamic model comes too close to apedestrian, then weights may be adjusted to increase penalty for speed,acceleration, and acceleration increases.

At the configuration adjustment module, a plurality of permutations ofthe one or more weights can be evaluated until the scores reach adesired range. Those one or more weights that generate the scores in thedesired range can be taken as the one or more weights of the adjustedcost function. At new configuration generator 614, an adjusted costfunction is generated with the adjusted weights. The adjusted costfunction is then used as the active cost function. In other words, theMPC controller with the adjusted cost function can then generate anothercontrol command.

The control command that is fed to vehicle controls can be the first ofa sequence of another sequence of control commands generated by theupdated MPC controller. As described, the MPC controller generates thesequence of control commands through optimizing terms by minimizing acost function (in this case, the adjusted cost function) whilesatisfying the target control objective. The target control objectivehere is to track along a reference trajectory.

An ADV system is shown in FIG. 7 having a control module 306 that uses alearning based MPC module to control the ADV. Planning module 305 cangenerate a path or route that the control module uses as a reference.This path or route can be determined by the planning module based on areference line, as described in other sections. The path or route can betaken as the target trajectory for which learning based MPC module 602will try to track.

As discussed, learning based MPC module can adjust dynamically (e.g.,while the ADV is driving) to account for real-time environmentalconditions of the ADV. These environmental conditions can be gatheredfrom servers 104 and 103, as well as localization module 301, map androute information 311, sensor system 115, and other modules. Afteradjusting to the environment, and accounting for physical attributes ofthe ADV, the MPC module can generate and optimized control command(e.g., throttle, steering, and/or brake) to be communicated to thecontrol system 111.

It should be understood that the MPC can generate, for a single drivingcycle, a throttle, steering, and/or brake command. In other words, for asingle driving cycle, multiple commands can be generated (for differentcontrol units) and communicated to respective control units (e.g.,steering actuator, throttle actuator, brake actuator) to effect acorresponding and proportionate movement of the ADV.

The sensor system 115 can feed ADV information back to the controlmodule 306, such as, for example, current heading, current steering,current speed, etc. The learning based MPC module can self-adjustperiodically, so that the control module generates control commands thataccount for environmental factors around the ADV and physical attributesof the ADV that may change over time.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A method for operating an autonomous drivingvehicle (ADV), the method comprising: generating a control command withan MPC (model predictive control) controller, the MPC controllerincluding an optimizer using a cost function configured with one or moreweights associated with cost terms of the cost function; applying thecontrol command to a dynamic model of the ADV to simulate a behavior ofthe ADV; adjusting the one or more weights based on an evaluation of thedynamic model in response to the control command, resulting in anadjusted cost function of the MPC controller; and generating, with theMPC controller having the adjusted cost function, a second controlcommand used to effect movement of the ADV.
 2. The method of claim 1,wherein the evaluation of the dynamic model is performed with respect toone or more current environmental conditions around the ADV, includingat least one of: a pedestrian, another vehicle, a road boundary, or astructure, weather, road wetness or slip, and vehicle traffic.
 3. Themethod of claim 2, wherein adjusting the one or more weights includesincreasing one of the one or more weights that is associated with asteering cost term, to penalize steering, in response to the one or morecurrent environmental conditions indicating the road wetness or theslip.
 4. The method of claim 2, wherein adjusting the one or moreweights includes decreasing one of the one or more weights that isassociated with a speed cost term, to increase speed more aggressively,in response to the one or more current environmental conditionsindicating low vehicle traffic.
 5. The method of claim 2, whereinadjusting the one or more weights includes decreasing one of the one ormore weights that is associated with a brake cost term, to reducepenalty for braking, in response to the one or more currentenvironmental conditions indicating high vehicle traffic.
 6. The methodof claim 1, wherein the dynamic model of the ADV simulates behavior ofthe ADV in a virtual environment, the virtual environment including atwo-dimensional or three-dimensional representation of a currentenvironment around the ADV.
 7. The method of claim 1, wherein thedynamic model of the autonomous driving vehicle includes one or morephysical attributes of the ADV, including at least one of: brakecondition, passengers or cargo in the ADV, battery state of charge, oran amount of fuel, wherein the one or more physical attributes isrepresentative of a current state of the ADV.
 8. The method of claim 1,wherein the MPC controller includes a simplified model of the ADV usedwith the cost function to generate an optimized sequence of controlcommands, the simplified model of the ADV having less accurate of arepresentation of the ADV than the dynamic model of the autonomousdriving vehicle.
 9. The method of claim 1, wherein the evaluation of thedynamic model in response to the control command is based on at leastone of: proximity of the dynamic model to a pedestrian, another vehicle,or a structure; a speed of the dynamic model with reference to a speedconstraint; an acceleration of the dynamic model with respect to anacceleration constraint; a location of the dynamic model with respect toa road boundary or path; changes in heading, or speed that can causeride discomfort; and control effort.
 10. The method of claim 1, whereinthe control command includes at least one of: a throttle command, asteering command, a brake command.
 11. The method of claim 1, whereinthe cost terms of the cost function include at least one of: cross-trackerror, heading error, speed cost, steering cost, acceleration cost,steering rate of change, braking, and acceleration rate of change. 12.The method of claim 1, wherein generating the control command includesapplying the MPC controller to a target control objective to generate asequence of control commands, and taking, as the control command, afirst of the sequence of control commands, the sequence of controlcommands being optimized by minimizing the cost function whilesatisfying the target control objective.
 13. The method of claim 12,wherein generating the second control command includes applying the MPCcontroller having the adjusted cost function to the target controlobjective to generate a second sequence of control commands, and taking,as the second control command, a first of the second sequence of controlcommands, the second sequence of control commands being optimized byminimizing the adjusted cost function while satisfying the targetcontrol objective.
 14. A non-transitory machine-readable medium havinginstructions stored therein, which when executed by a processor, causethe processor to perform operations of operating an autonomous drivingvehicle (ADV), the operations comprising: generating a control commandwith an MPC (model predictive control) controller, the MPC controllerincluding an optimizer using a cost function configured with one or moreweights associated with cost terms of the cost function; applying thecontrol command to a dynamic model of the ADV to simulate a behavior ofthe ADV; adjusting the one or more weights based on an evaluation of thedynamic model in response to the control command, resulting in anadjusted cost function of the MPC controller; and generating, with theMPC controller having the adjusted cost function, a second controlcommand used to effect movement of the ADV.
 15. The non-transitorymachine-readable medium of claim 14, wherein the evaluation of thedynamic model is performed with respect to one or more currentenvironmental conditions around the ADV, including at least one of: apedestrian, another vehicle, a road boundary, or a structure, weather,road wetness or slip, and vehicle traffic.
 16. The non-transitorymachine-readable medium of claim 15, wherein adjusting the one or moreweights includes increasing one of the one or more weights that isassociated with a steering cost term, to penalize steering, in responseto the one or more current environmental conditions indicating the roadwetness or the slip.
 17. A data processing system, comprising: aprocessor; and a memory coupled to the processor to store instructions,which when executed by the processor, cause the processor to performoperations of operating an autonomous driving vehicle (ADV), theoperations including generating a control command with an MPC (modelpredictive control) controller, the MPC controller including anoptimizer using a cost function configured with one or more weightsassociated with cost terms of the cost function; applying the controlcommand to a dynamic model of the ADV to simulate a behavior of the ADV;adjusting the one or more weights based on an evaluation of the dynamicmodel in response to the control command, resulting in an adjusted costfunction of the MPC controller; and generating, with the MPC controllerhaving the adjusted cost function, a second control command used toeffect movement of the ADV.
 18. The data processing system of claim 17,wherein the evaluation of the dynamic model is performed with respect toone or more current environmental conditions around the ADV, includingat least one of: a pedestrian, another vehicle, a road boundary, or astructure, weather, road wetness or slip, and vehicle traffic.
 19. Thedata processing system of claim 18, wherein adjusting the one or moreweights includes increasing one of the one or more weights that isassociated with a steering cost term, to penalize steering, in responseto the one or more current environmental conditions indicating the roadwetness or the slip.
 20. The data processing system of claim 18, whereinadjusting the one or more weights includes decreasing one of the one ormore weights that is associated with a speed cost term, to increasespeed more aggressively, in response to the one or more currentenvironmental conditions indicating low vehicle traffic.